Multi-attribute reverse auctions and negotiations with verifiable and not-verifiable offers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Comparative studies of auction and negotiation exchange mechanisms have typically compared the outcomes obtained from the two mechanisms. Their result are inconclusive. The question which this paper aims to address is the viability of outcome-based comparisons. Such comparisons assume that both mechanisms produce the same types of outcomes but their values differ. An argument can be made that this is not necessarily the case. Based on several experiments of multi-attribute auctions and two formats of multi-bilateral negotiations the paper argues that the two mechanisms produce some outcomes which are comparable and other outcomes which are qualitatively different. A surprising finding of our experiments is that the outcomes of the non-verifiable negotiations were more similar to the outcomes of the reverse auctions than to the verifiable negotiations, despite the fact that the latter employ rules taken from the auction mechanism.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it